/*
 *    This program is free software; you can redistribute it and/or modify
 *    it under the terms of the GNU General Public License as published by
 *    the Free Software Foundation; either version 2 of the License, or
 *    (at your option) any later version.
 *
 *    This program is distributed in the hope that it will be useful,
 *    but WITHOUT ANY WARRANTY; without even the implied warranty of
 *    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *    GNU General Public License for more details.
 *
 *    You should have received a copy of the GNU General Public License
 *    along with this program; if not, write to the Free Software
 *    Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
 */

/*
 *    Clusterer.java
 *    Copyright (C) 1999 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.clusterers;

import weka.core.Capabilities;
import weka.core.Instance;
import weka.core.Instances;

/**
 * Interface for clusterers. Clients will typically extend either
 * AbstractClusterer or AbstractDensityBasedClusterer.
 * 
 * @author Mark Hall (mhall@cs.waikato.ac.nz)
 * @revision $Revision: 1.18 $
 */
public interface Clusterer {

	/**
	 * Generates a clusterer. Has to initialize all fields of the clusterer that
	 * are not being set via options.
	 * 
	 * @param data
	 *            set of instances serving as training data
	 * @exception Exception
	 *                if the clusterer has not been generated successfully
	 */
	void buildClusterer(Instances data) throws Exception;

	/**
	 * Classifies a given instance. Either this or distributionForInstance()
	 * needs to be implemented by subclasses.
	 * 
	 * @param instance
	 *            the instance to be assigned to a cluster
	 * @return the number of the assigned cluster as an integer
	 * @exception Exception
	 *                if instance could not be clustered successfully
	 */
	int clusterInstance(Instance instance) throws Exception;

	/**
	 * Predicts the cluster memberships for a given instance. Either this or
	 * clusterInstance() needs to be implemented by subclasses.
	 * 
	 * @param instance
	 *            the instance to be assigned a cluster.
	 * @return an array containing the estimated membership probabilities of the
	 *         test instance in each cluster (this should sum to at most 1)
	 * @exception Exception
	 *                if distribution could not be computed successfully
	 */
	public double[] distributionForInstance(Instance instance) throws Exception;

	/**
	 * Returns the number of clusters.
	 * 
	 * @return the number of clusters generated for a training dataset.
	 * @exception Exception
	 *                if number of clusters could not be returned successfully
	 */
	int numberOfClusters() throws Exception;

	/**
	 * Returns the Capabilities of this clusterer. Derived classifiers have to
	 * override this method to enable capabilities.
	 * 
	 * @return the capabilities of this object
	 * @see Capabilities
	 */
	public Capabilities getCapabilities();

}